
"""simple docstring"""

import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import DecisionTransformerModel
   from transformers.models.decision_transformer.modeling_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=6 ,							lowercase_=1_7 ,							lowercase_=2_3 ,							lowercase_=1_1 ,							lowercase_=True ,							)    ->       int:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       seq_length
          UpperCAmelCase          =       act_dim
          UpperCAmelCase          =       state_dim
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       max_length
          UpperCAmelCase          =       is_training




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       floats_tensor((self.batch_size, self.seq_length, self.state_dim)				)
          UpperCAmelCase          =       floats_tensor((self.batch_size, self.seq_length, self.act_dim)				)
          UpperCAmelCase          =       floats_tensor((self.batch_size, self.seq_length, 1)				)
          UpperCAmelCase          =       floats_tensor((self.batch_size, self.seq_length, 1)				)
          UpperCAmelCase          =       ids_tensor((self.batch_size, self.seq_length) ,							vocab_size=1_0_0_0				)
          UpperCAmelCase          =       random_attention_mask((self.batch_size, self.seq_length)				)

          UpperCAmelCase          =       self.get_config()

          return (
              config,
              states,
              actions,
              rewards,
              returns_to_go,
              timesteps,
              attention_mask,
          )




      def      a_   (				self				)    ->       List[str]:
          return DecisionTransformerConfig(
              batch_size=self.batch_size ,							seq_length=self.seq_length ,							act_dim=self.act_dim ,							state_dim=self.state_dim ,							hidden_size=self.hidden_size ,							max_length=self.max_length ,							)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Union[str, Any]:
          UpperCAmelCase          =       DecisionTransformerModel(config=_UpperCAmelCase				)
          model.to(_UpperCAmelCase				)
          model.eval()
          UpperCAmelCase          =       model(_UpperCAmelCase ,							_UpperCAmelCase ,							_UpperCAmelCase ,							_UpperCAmelCase ,							_UpperCAmelCase ,							_UpperCAmelCase				)

          self.parent.assertEqual(result.state_preds.shape ,							states.shape				)
          self.parent.assertEqual(result.action_preds.shape ,							actions.shape				)
          self.parent.assertEqual(result.return_preds.shape ,							returns_to_go.shape				)
          self.parent.assertEqual(
              result.last_hidden_state.shape ,							(self.batch_size, self.seq_length * 3, self.hidden_size)				)  # seq length *3 as there are 3 modelities: states, returns and actions




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase	
          )      ,				(
              UpperCAmelCase	
          )      ,				(
              UpperCAmelCase	
          )      ,				(
              UpperCAmelCase	
          )      ,				(
              UpperCAmelCase	
          )      ,				(
              UpperCAmelCase	
          )      ,				(
              UpperCAmelCase	
          )      ,					
          )          =       config_and_inputs
          UpperCAmelCase          =       {
              'states': states,
              'actions': actions,
              'rewards': rewards,
              'returns_to_go': returns_to_go,
              'timesteps': timesteps,
              'attention_mask': attention_mask,
          }
          return config, inputs_dict

@require_torch
class   _UpperCAmelCase				(		snake_case__  ,      snake_case__  ,      snake_case__  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				List[str]				   =						(DecisionTransformerModel,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						()
      __SCREAMING_SNAKE_CASE :				int				   =						{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

      # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						False

      # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
      __SCREAMING_SNAKE_CASE :				int				   =						False
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						False
      __SCREAMING_SNAKE_CASE :				Tuple				   =						False
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						False
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						False
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						False
      __SCREAMING_SNAKE_CASE :				Tuple				   =						False
      __SCREAMING_SNAKE_CASE :				Dict				   =						False
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						False




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       DecisionTransformerModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=_UpperCAmelCase ,							hidden_size=3_7				)




      def      a_   (				self				)    ->       str:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase				)




      @slow
      def      a_   (				self				)    ->       Optional[int]:
          for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase          =       DecisionTransformerModel.from_pretrained(_UpperCAmelCase				)
              self.assertIsNotNone(_UpperCAmelCase				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase          =       model_class(_UpperCAmelCase				)
              UpperCAmelCase          =       inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase          =       [*signature.parameters.keys()]

              UpperCAmelCase          =       [
                  'states',
                  'actions',
                  'rewards',
                  'returns_to_go',
                  'timesteps',
                  'attention_mask',
              ]

              self.assertListEqual(arg_names[: len(_UpperCAmelCase				)] ,							_UpperCAmelCase				)

@require_torch
class   _UpperCAmelCase				(		unittest.TestCase				):




      @slow
      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       2  # number of steps of autoregressive prediction we will perform
          UpperCAmelCase          =       1_0  # defined by the RL environment, may be normalized
          UpperCAmelCase          =       DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'				)
          UpperCAmelCase          =       model.to(_UpperCAmelCase				)
          UpperCAmelCase          =       model.config
          torch.manual_seed(0				)
          UpperCAmelCase          =       torch.randn(1 ,							1 ,							config.state_dim				).to(device=_UpperCAmelCase ,							dtype=torch.floataa				)  # env.reset()

          UpperCAmelCase          =       torch.tensor(
              [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]] ,							device=_UpperCAmelCase				)

          UpperCAmelCase          =       torch.tensor(_UpperCAmelCase ,							device=_UpperCAmelCase ,							dtype=torch.floataa				).reshape(1 ,							1 ,							1				)
          UpperCAmelCase          =       state
          UpperCAmelCase          =       torch.zeros(1 ,							0 ,							config.act_dim ,							device=_UpperCAmelCase ,							dtype=torch.floataa				)
          UpperCAmelCase          =       torch.zeros(1 ,							0 ,							device=_UpperCAmelCase ,							dtype=torch.floataa				)
          UpperCAmelCase          =       torch.tensor(0 ,							device=_UpperCAmelCase ,							dtype=torch.long				).reshape(1 ,							1				)

          for step in range(_UpperCAmelCase				):
              UpperCAmelCase          =       torch.cat([actions, torch.zeros(1 ,							1 ,							config.act_dim ,							device=_UpperCAmelCase				)] ,							dim=1				)
              UpperCAmelCase          =       torch.cat([rewards, torch.zeros(1 ,							1 ,							device=_UpperCAmelCase				)] ,							dim=1				)

              UpperCAmelCase          =       torch.ones(1 ,							states.shape[1]				).to(dtype=torch.long ,							device=states.device				)

              with torch.no_grad():
                  UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       model(
                      states=_UpperCAmelCase ,							actions=_UpperCAmelCase ,							rewards=_UpperCAmelCase ,							returns_to_go=_UpperCAmelCase ,							timesteps=_UpperCAmelCase ,							attention_mask=_UpperCAmelCase ,							return_dict=_UpperCAmelCase ,							)

              self.assertEqual(action_pred.shape ,							actions.shape				)
              self.assertTrue(torch.allclose(action_pred[0, -1] ,							expected_outputs[step] ,							atol=1E-4				)				)
              UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       (  # env.step(action)
                  torch.randn(1 ,							1 ,							config.state_dim				).to(device=_UpperCAmelCase ,							dtype=torch.floataa				),
                  1.0,
                  False,
                  {},
              )

              UpperCAmelCase          =       action_pred[0, -1]
              UpperCAmelCase          =       torch.cat([states, state] ,							dim=1				)
              UpperCAmelCase          =       returns_to_go[0, -1] - reward
              UpperCAmelCase          =       torch.cat([returns_to_go, pred_return.reshape(1 ,							1 ,							1				)] ,							dim=1				)
              UpperCAmelCase          =       torch.cat(
                  [timesteps, torch.ones((1, 1) ,							device=_UpperCAmelCase ,							dtype=torch.long				) * (step + 1)] ,							dim=1				)








'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE__     :						List[str]	=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__     :						Dict	=  {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						Optional[int]	=  {
    '''facebook/bart-base''': 1_0_2_4,
    '''facebook/bart-large''': 1_0_2_4,
    '''facebook/bart-large-mnli''': 1_0_2_4,
    '''facebook/bart-large-cnn''': 1_0_2_4,
    '''facebook/bart-large-xsum''': 1_0_2_4,
    '''yjernite/bart_eli5''': 1_0_2_4,
}

class 							a__( snake_case__     ):
			a_	: Dict									=      VOCAB_FILES_NAMES
			a_	: Tuple									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: Tuple									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: List[Any]									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      BartTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="replace"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=False					,   _UpperCAmelCase=True					,   **_UpperCAmelCase					,   )							->							int:
					super().__init__(
					    _UpperCAmelCase					,   _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   errors=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   add_prefix_space=_UpperCAmelCase					,   trim_offsets=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'					,   _UpperCAmelCase						) != add_prefix_space:
							snake_case__	     =getattr(_UpperCAmelCase					,   pre_tok_state.pop('type'						)						)
							snake_case__	     =add_prefix_space
							snake_case__	     =pre_tok_class(**_UpperCAmelCase						)

					snake_case__	     =add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					snake_case__	     ='post_processor'
					snake_case__	     =getattr(self.backend_tokenizer					,   _UpperCAmelCase					,   _UpperCAmelCase						)
					if tokenizer_component_instance:
							snake_case__	     =json.loads(tokenizer_component_instance.__getstate__()						)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
									snake_case__	     =tuple(state['sep']						)
							if "cls" in state:
									snake_case__	     =tuple(state['cls']						)

							snake_case__	     =False

							if state.get('add_prefix_space'					,   _UpperCAmelCase						) != add_prefix_space:
									snake_case__	     =add_prefix_space
									snake_case__	     =True

							if state.get('trim_offsets'					,   _UpperCAmelCase						) != trim_offsets:
									snake_case__	     =trim_offsets
									snake_case__	     =True

							if changes_to_apply:
									snake_case__	     =getattr(_UpperCAmelCase					,   state.pop('type'						)						)
									snake_case__	     =component_class(**_UpperCAmelCase						)
									setattr(self.backend_tokenizer					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			@property
			def 						_lowercase  (     self						)							->							str:
					if self._mask_token is None:
							if self.verbose:
									logger.error('Using mask_token, but it is not set yet.'						)
							return None
					return str(self._mask_token						)






			@mask_token.setter
			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							int:
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else value
					snake_case__	     =value






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							BatchEncoding:
					snake_case__	     =kwargs.get('is_split_into_words'					,   _UpperCAmelCase						)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    'to use it with pretokenized inputs.'						)

					return super()._batch_encode_plus(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							BatchEncoding:
					snake_case__	     =kwargs.get('is_split_into_words'					,   _UpperCAmelCase						)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    'to use it with pretokenized inputs.'						)

					return super()._encode_plus(*_UpperCAmelCase					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					snake_case__	     =self._tokenizer.model.save(_UpperCAmelCase					,   name=_UpperCAmelCase						)
					return tuple(_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=None						)							->							Optional[int]:
					snake_case__	     =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
							return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




